- Whilst there have been several advocates for the
application of software engineering (SE) methodologies in the
development of agent-based models and simulations in the social
sciences, the uptake of these techniques in the research community has
been limited – or if authors are using such techniques, their use is
underreported. Software engineering provides structured processes and
techniques for designing, documenting, implementing and testing
computer software. Software processes have many variations, each with
their own unique advantages and disadvantages depending on the
constraints (such as: human resources, time, finance, quality) facing a
project team. This paper sets out the methods of Scrum agile software
development, and discusses the experience of using Scrum to organise
workflow and guide the development of an agent-based model of alcohol
consumption. By employing Scrum in conjunction with another software
engineering method, the Unified Modelling Language, this paper
represents a case study in SE methods applied to a real world research
- Agile, Agent-Based, Alcohol, Attitudes, Microsimulation, Modelling
- Agent-based modelling and simulation (ABMS) is a method of
computational research with an established history in fields as diverse
as economics (Cristelli et al.
2011), biology (An et al. 2009),
and social science (Gilbert 2007)
where it is often labelled agent-based social simulation (ABSS).
Computer programs that perform ABSS are examples of simulation
- In order to be used with confidence as a scientific
instrument, it is important that any software be engineered with
quality and rigor; however, not all developers of simulation software
have the experience or knowledge of best practices for software
development, although some introductory guides exist outside of the
software development literature that are specifically related to
creating software for scientific research (Baxter
et al. 2006; Wilson et
- The discipline relating to the practice of principled
software creation is known as Software Engineering (SE). Despite the
wealth of available literature on SE, the plethora of SE techniques
have yet to receive widespread acceptance and adoption within the
context of ABMS and ABSS (Klügl 2003).
Principled SE offers many benefits to ABSS developers. Its overriding
purpose is to facilitate the development of software, ensuring that
software can be used, updated and extended upon by individuals other
than those involved in its development (Sommerville
2009). The term computer program is often used synonymously
with the word software; however, software is much more, and is intended
to represent all of the relevant data and documentation associated with
the operation of a given computer program.
- In this paper we will present a case study in the use of a
software process known as agile development for the
purpose of developing a computational model of alcohol consumption. We
will first introduce software engineering and explain why academia has
unique challenges in relation to the production and maintenance of
software. We will then describe some of the techniques that agile
methods provide that may be of use to those developing software in an
academic environment, with a particular focus on a variant called Scrum
- After motivating the use of complex systems methods for the development of a model of alcohol consumption, we will describe our modelling methodology and explain how Scrum was used as a means of driving our project through fostering stakeholder engagement and team communication, organising work flow, and increasing developer productivity. We will then reflect on the use of Scrum for our project. The use of established software engineering practices such as the Unified Modelling Language (OMG 2011) to document and design simulation software will also be discussed in the context of our alcohol modelling case study. Finally, we will briefly present our vision for the research theme, building upon our current model to incorporate social dynamics into future software iterations, whilst adhering to the agile method.
Software Development, Processes, and Applicability in an Academic Setting
Software development in academia
- In the academic world software is developed for a wide
variety of purposes, and by individuals or groups with a wide range of
previous expertise in the production of software. Researchers often
develop code as individuals, or in small teams, and perhaps with their
own programming styles. These developers may or may not be aware of the
various methods and processes that have been established for best
practice in creating, documenting and maintaining software.
Documentation is quite often a necessity to enable future staff
employed on the project to become familiar with a pre-existing piece of
software, though planning and documenting software involves a
significant investment of time and resources.
- Several issues face those who develop software for academic
purposes. The unstable nature of academic software projects, with the
uncertainty of funding timescales and staffing arrangements (e.g. via
fixed term contracts), places a demanding time constraint on the
development process, and creates problems for the future maintenance
and extension of existing projects and code-bases (Pitt-Francis et al. 2008).
- Developers make decisions on how to create software
conditional on a range of factors, including prior experience and
current resources (time, human resources, finances). Quite often
developers, regardless of experience, are often tempted to code without
a plan regarding how they will manage their resources; writing a
program, its functions and algorithms on the fly until an apparently
working program emerges. Such an approach has many critical
disadvantages. Firstly, without specifying what the software is
required to do, or a list of features
(distinguishing software characteristics or functionality), it is not
possible to measure objectively whether or not the software fulfils its
purpose. Secondly, without a description of how or why the software has
been designed or constructed, future users or developers of the
software may struggle to maintain, extend or test it. Thirdly, without
any consideration for time-management, it becomes extremely difficult
to deliver a quality software product with all of the desired
functionality and within the duration of the project. SE approaches
address these issues by forcing developers to either formally document
the software requirements and design, or by encouraging teams to work
closely with customers and stakeholders to iteratively develop
software; with customer or user feedback guiding future software
iterations. SE also often adopts principles from project management,
providing methods for structuring the various aspects of the
- A plethora of software processes exist that those
developers with knowledge of SE might be aware of. Software processes
are frameworks that contain a set of discrete activities that aid the
software development process (Sommerville
2009), and one of the more traditional software process
frameworks is the Waterfall model. The Waterfall
model is a set of activities that are tackled in a linear fashion.
specification of requirements is created, which are
then translated into a set of software requirements
and a system/architectural software design
constructed to fulfil those requirements. The design is subsequently
programmed into computer code in the implementation
phase. Finally, some form of verification is
performed to ensure the implementation satisfies the design and
requirements. Verification activities can be either static (e.g. code
checks, formal verification of algorithms) or dynamic checks (e.g.
actively testing the functionality of the program and/or its
constituent components) (Dasso
& Funes 2007).
- It can be argued that the basic waterfall model described
above, alongside similar variants, is particularly inflexible as a
paradigm for developing software when project variables are subject to
a degree of change; as each activity in the waterfall model must be
complete before moving onto the next. With any software project, there
may be design errors, or a change in requirements, and with such a
linear approach it can become time-consuming to reconstruct designs,
and update all of the relevant project documents and outputs. The
dynamic nature of academic research has additional concerns that make
it incompatible in some respects with such a linear approach to
development. New insights are often gleaned, or the research changes
direction due to new data or hypotheses. It then becomes more difficult
to introduce design changes with such a heavily front-loaded approach
to requirements specification and design.
- Several alternative software process models exist,
including but not limited to: rapid application development
(RAD), incremental development and spiral,
each with their own advantages and disadvantages given the specific
context in which they will be used (Sommerville
2009). An alternative set of principles that takes
inspiration from some of these processes is a group of methods known as
agile development methods, which contain several
techniques that can address many of the issues facing those developing
software in an academic setting.
Agile development methods
- The over-riding principles of agile development are
iteration and flexibility. Agile is a people-oriented approach that
centres on prioritising the requirements dictated by the project's
various stakeholders and agreed by the developers. Iterative in nature,
agile requires a workforce that is dynamic to change, which can be
introduced more rapidly with smaller development teams such as those in
an academic setting. Agile may not be the best method as team sizes
scale, and there are those that believe agile methods are not suited to
larger development teams (Cohen et
- The values of agile are documented in the agile
for Agile Software Development 2001), and one of those values
relates to software documentation: agile practitioners believe that working
software takes priority over comprehensive
documentation. Practitioners of agile often therefore adopt a
lean approach to documentation, and rely on the
knowledge of experienced team members to pass on information relating
to software design and implementation to new developers (Pitt-Francis et al. 2008).
Such a practice assumes that there will always be someone working on a
project who has intimate knowledge of its details, which may not be the
case with academic projects, which may suffer from periods of
inactivity or changing staffing arrangements. Therefore, it is up to
the developers to decide on the appropriate level of documentation
required, without significantly impacting on the agility of the team.
- There are several variations on the agile themed approach,
each recommending its own set of activities to be performed. A recent
systematic review describes the body of agile development literature,
and the reader is directed there for a more comprehensive review of the
various methods available (Dybå and
Dingsøyr 2008). In realising the simulation software
presented later in this paper, we took inspiration from the Scrum
development method (Schwaber
& Sutherland 2013). Scrum inherits many themes from iterative
and incremental development (Larman
& Basili 2003), and the process actively assumes that
the development cycle will be turbulent and changing, and provides
methods that are adaptive to those changing project requirements (Schwaber 1997).
- Scrum comprises three core components: (1) the Product
Backlog; (2) the Sprint Backlog; and (3)
the Working Software Increment (Figure 1). Firstly a Product Backlog is
created, comprising all of the known features required in the final
software product. Since Scrum is iterative, the Product Backlog is not
a fixed set of features or requirements. The backlog can be adapted
depending on the changing requirements of the relevant stakeholders, or
due to practical or technical issues, for example bug fixing (e.g.
arithmetic, logic, or syntax errors in the code) of the current Working
Software Increment, or implementing previously unplanned yet newly
essential or desirable features. Secondly, a Sprint Backlog is created,
providing a prioritised list of the desired software work items and
features for the upcoming Sprint. A Sprint is simply a dedicated cycle
of work with a planned duration of time to implement items from the
Sprint Backlog; typically a Sprint lasts between 1–4 weeks in duration.
During each Sprint, the project team should ideally have a Daily Scrum,
a short meeting to plan work activities for the day. The Working
Software Increment is the product of each Sprint and should have useful
functionality that can be discussed by the project team in order to
inform future iterations of the software.
- Alongside the various Scrum components, there are three
roles in any Scrum project. The Product Owner
represents the customer or any stakeholders with an interest in the
final product. The Development Team is an umbrella
term for all individuals involved in the software development of the
product. Finally, the Scrum Master ensures that the
Scrum process is adhered to, and prioritises features to be included in
the next Sprint cycle.
- A target is set for each Sprint, called the Sprint
Goal. Each Sprint Goal is agreed upon during a Sprint
Planning meeting, which is an extended meeting to establish
what will be implemented in the next Sprint, and how the team will
accomplish that implementation (). At the
end of a Sprint, a Sprint Review is undertaken to
evaluate the current Working Software Increment, detailing to the
stakeholders what has been accomplished, and providing an opportunity
for the wider team to be updated on the current status of the project.
The wider team represents those individuals who may contribute to
certain aspects of the project, perhaps having been called on for their
expertise, but are not involved in the day-to-day running of the
project or have ultimate responsibility for delivery of project aims
- A Scrum Planning Board is created to keep track of project
tasks at various stages of development. The planning board can be
implemented physically, with a whiteboard and sticky notes, providing
an excellent focal point for a team to engage in their Scrum meetings.
Alternatively, there are a wide variety of online tools and graphical
packages that can be used to implement a Scrum board collaboratively or
otherwise. Either the physical or graphical planning board method
provides a visual means of keeping track of the project, organised by
Product Backlog, Sprint Backlog, in progress (features of current
Sprint) and completed features. For a team or lone developer, having a
planning board allows active visualisation of the flow of activities
day-to-day, providing a positive sense of accomplishment as tasks move
from the backlog to completion. Should items linger in the backlog for
too long, rather than become disheartened, developers may take the time
to re-evaluate the status of those items according to whatever
prioritization method the project team agrees on, thus maintaining
feature flow and productivity.
- Scrum as a method has been adopted by academics within
several different contexts. Specific to developing software for
research, the Cancer, Heart and Soft Tissue Environment (CHASTE) was
developed to provide a simulation framework suitable for computational
models within biology (Mirams et al.
2013). The CHASTE project team recognised the pitfalls of
developing software within academia and chose to adhere to a
test-driven agile approach, focusing on activities such as pair
programming (two developers coding together at one computer) which they
argued helps team cohesion and promotes collective ownership (Pitt-Francis et al. 2009).
In another study, a novel Scrum approach to managing a research group
comprising a cohort of postgraduate research students was developed (Hicks & Foster 2010a).
The approach, known as Scrum for Research (SCORE), encourages regular
(three-times-per-week) status updates where the whole group is in
attendance, and recommends more in-depth on-demand
meetings for any immediate challenges that arise for group members (Hicks & Foster 2010b).
The authors report that SCORE increased group productivity, allowed
ideas to be shared and elaborated upon rapidly, and helped students
cope with stresses relating to feeling a lack of progress with their
individual works. Within the field of pedagogy, Scrum has also been
used to structure a games development course according to a set of
design (pre-production) and Sprint (game production) activities, and to
promote teamwork through regular team interaction (Schild et al 2010). The
flexible, iterative Scrum approach suited the evolving nature of games
development, allowing prototype development to feed new requirements
for the Product Backlog, whilst organising the project workload into
Sprints designed to fit with the schedule of the curriculum. The
diverse range of applicable areas in which agile can be applied to
academia makes it a promising method for software development purposes
and more generally, project management.
Figure 1. The Scrum process. Stakeholders (including developers) agree upon the required features and functionality of the final software product and these comprise the Product Backlog. The stakeholders have a Sprint Planning meeting to determine those work items from the Product Backlog that will form the Sprint Backlog. Work items are then implemented from the Sprint Backlog by the developers in time-boxed Sprints. Stakeholders review the Working Software Increment during a Sprint Review meeting, and this informs the development of future software iterations.
Unified Modelling Language
- As previously discussed, the traditional lean
approach to documentation with agile assumes project members will
remain to impart their knowledge to future developers, which whilst
ideal is not always possible in academia. In these instances,
documentation is necessary to ensure the long-term viability of the
project independent of specific developers.
- One method of describing and documenting software is to use
the Unified Modelling Language (UML) (OMG
2011). UML, traditionally
used in the field of software engineering, has 14 separate diagrams (as
of UML v2.4) that can be used to visually model a system, its objects
and their relationships. The utility of each and every UML diagram is a
matter of debate, and their utility is ultimately down to the belief of
the modeller in each diagram's ability as a communicative medium. There
are several proponents of UML who describe both benefits and pitfalls
of UML notation for the design of ABMs of a variety of systems (Bauer & Odell 2005; Bersini 2006, 2012; Read
et al. 2009; Alden et al.
- Verification, the act of ensuring that software adheres to
its specification, is an important activity in the development of any
software – this is particularly important when the proposed software is
to be used for the purposes of scientific experimentation. Unit testing
is one method of software verification, and is central to many software
development methodologies including the agile approach known as Extreme
Programming (XP). Unit tests can be written to determine whether the
various methods and functions that comprise the software operate as
expected and required. Unit tests also may provide valuable insight to
developers new to an existing software project. Tests are generally
designed to expose the software to both valid and invalid input data,
and act in part as a form of documentation for the software, or at
least for the constituent components for which unit tests have been
- Another common practice, essential for the preservation and
maintenance of code during the software project life cycle, is revision
control (RC), allowing the storage, management and documentation of
changes to code (Osborne et al. 2014).
The key benefit of RC is that it provides a stable means of modifying a
project's code base without overwriting previous versions of that code.
This is achieved by creating new versions of a set of initial files,
called a revision. Each individual file can be
reverted to, or merged with, any previous revision. Should the
developer have a body of code associated with a working piece of
software, to avoid breaking the existing code, a branch of duplicate
code can be create from the existing code base. Updates are then
applied to the new branch and these changes can be later merged into
the original branch, subsequent to the relevant testing and
verification, or be kept as stand-alone software versions called forks.
Some RC tools provide the ability to chart the evolution of a software
product visually, providing a valuable resource to current and future
developers, detailing all of the software revisions and forks.
- Online source code repositories are available for RC, and several of these allow users to open up their code to the public, lending transparency to the scientific process by allowing researchers to more easily share their code and provide insight into its development (Prlic & Procter 2012), and also facilitating reproducibility of simulation research (Sandve et al. 2013). Commonly used repositories include GitHub (GitHub, Inc. www.github.com) and Bitbucket (Atlassian www.bitbucket.org). Repository providers have their own individual pricing strategies and offer a range of additional features, though most have a free to use option available.
Complex Systems Modelling of Alcohol Consumption Dynamics: A Case Study in Scrum
- The research detailed in this paper represents a work
package within an academic research project: Complex Systems
Modelling of Alcohol Consumption Dynamics in the British Population
(CSMACD), funded by the UK Economic and Social Research
Council. The aim of the work package was to identify empirically
validated, causally-driven, micro-simulations that bridge the gap
between micro-social (individual-level) and macro-social
(population-level) knowledge of the dynamics of drinking
- Whilst computational models are relatively well established
within the field of alcohol policy appraisal, contemporary models tend
to assume that consumption change is purely a function of ageing (Chisholm 2004; Hollingworth et al. 2006;
Purshouse et al.
2014). However recent age-period-cohort analyses have
indicated strong birth cohort and period effects: i.e. today's 45-54
year olds do not exhibit consumption patterns similar to 45–54 year
olds in previous decades (Meng et al.
). Furthermore, when
considering behaviour change arising from an intervention, these
contemporary models rely on neoclassical economics – specifically, they
assume that individual drinkers behave rationally, have access to
perfect information, and are able to maximize their own utility subject
to a budget constraint. The validity of these assumptions as a credible
generating mechanism for drinking change is questionable (Hedström 2005) and
ignore wider theories on personal and social factors that drive alcohol
as a complex system (Holder 1998;
Room et al. 2009). In
summary, no quantitative model has been established that convincingly
explains how individual-level drinking can manifest the observed
population-level trends in alcohol consumption patterns.
- We hypothesise that ABMS can allow us to provide new
insight into historical patterns of alcohol consumption, together with
potential predictive capability of future trends, by allowing us to
investigate how individual behaviours and social interactions might
influence the system. Such an approach would harness the benefits of
the agent-based approach within a social science context (Bonabeau 2002; Gilbert 2008) via the
emergence of complex macro-level patterns (i.e. alcohol consumption
trends) arising from the drinking behaviour of interacting individuals
at the micro-level (Bonabeau 2002;
Gilbert 2004; Hedström 2005).
- ABM represents a powerful analytical tool to understand
both causality and emergence within the social sciences (Hedström 2005) and
agent-based social network models have been used in a number of alcohol
related studies (Fowler 2009;
Ormerod & Wiltshire 2009;
& Crutzen 2013). Our initial goal was to ensure that
our model was data-driven and constructed using evidence-based
assumptions regarding individuals and the psychological drivers of
alcohol consumption, before moving towards a model including social
interaction. The reason for this is that in order for any complex
systems model to be accepted within the alcohol field, and to gain
acceptance for alcohol policy appraisal, it needs to have strong
data-driven and evidence-based foundations (Katikireddi et al. 2014).
Once such foundations are in place, we can then incorporate elements of
theory and hypothesis-driven experimentation regarding social
connectedness, social establishments and frameworks, to determine how
interactions influence drinking behaviour, as it is argued that a
combination of both social psychology and social interaction theory can
provide a greater level of explanatory power with regards to
sociological research (Hedström
Am agile approach to complex systems modelling of alcohol consumption dynamics
- An agile approach was chosen for the software development
stages of the project for two reasons. Firstly, the project was
designed to use secondary data (principally from the UK Data Archive)
that was not collected for the specific research questions to be
addressed by the study. Consequently, parameterisation issues were
anticipated when attempting to extract model inputs for theory-led (and
typically data hungry) ABMs (Boero
& Squazzoni 2005). These
issues induce attempts to negotiate or balance modelling requirements
relating to theory on the one hand and empirical validation on the
other: in other words, requirements will change. Secondly, the project
had a short (12 month) time constraint, requiring an approach that
allowed for rapid prototyping during the development stages (8 months).
We also wanted to develop a software framework that is extensible,
enabling the project to be maintained in future research efforts.
The project team
- The project team initially consisted of four members: the
principal investigator and lead modeller (Robin), the modeller and lead
developer (Daniel) and two domain experts – Paul, our social
psychologist, and Alan, a specialist in modelling and decision-making
in the health domain. These four members of our interdisciplinary
project team are the key stakeholders in the project. Alan, Daniel,
Robin and Paul decided the features for our Product Backlog. Our
primary developer Daniel acted as the Scrum master and prioritised
features for the next Sprint. Two additional researchers (Abdallah and
Mark) were called upon for their modelling expertise since the
inception of the project, and were considered part of the wider project
team, and did not play an active role in the Scrum process, though were
kept informed as to the status of the project as it progressed.
Prioritising software features
- To prioritise all possible model features, we adopted the
Must Should Could Would (MoSCoW) method (Clegg
& Barker 1994). MoSCoW is designed to ensure that
both developers and project stakeholders have agreed upon the
importance of the features of a piece of software. MoSCoW priority in
relation to CSMACD model development is as follows:
- Must have (MH) features are those that
are essential for our alcohol model to function, and to be able to
simulate an output measure related to consumption. These features
include those relating to: loading and parameterisation from
individual-level respondent data, algorithms to impute data from
additional data sets, functions to update the states in the model, and
methods required to extract useful data from the simulation.
- Should have (SH) features are those that
are not critical to the operation of our alcohol model; however, they
should eventually be implemented. These can include, for example,
additional output metrics (see section 3.4); functionality to perform
additional analyses on the model, such as analysing simulator
robustness to parameter perturbation; or implementing optimisation
algorithms to explore and optimise system parameterisations (Purshouse et al. 2014).
Refactoring may also feature as an SH activity. Refactoring is the act
of restructuring computer code without changing the overall function of
that code, and is a valuable and required activity for any developer.
Small scale refactoring, perhaps to improve the readability of a method
or algorithm, is required to facilitate re-usability and extensibility
of existing code. More large scale, and therefore time consuming,
refactoring activities may be required, though their importance may be
lower than more pressing features, and would thus fall into a lower
category of priority.
- Could have (CH) features are desirable
but can be omitted if project resources or time are scarce. As
highlighted as a SH feature, refactoring could also be a marked as a CH
feature if the activity requires substantial effort and resource to
accomplish. Note that since refactoring is not the act of changing the
function of the code, it is up to the developer to evaluate the
priority of such an activity. For example, if an algorithm had numerous
lines of duplicate code, a developer might wish to refactor to remove
the superfluous code from the implementation, thus improving
maintainability of the code and perhaps indirectly the efficiency of
their code through resource recovery. Depending on how detrimental the
original implementation is to the operation of the program, such a
refactoring activity might be more suited to a CH activity than a SH
activity, especially if development human resources are scarce.
- Would have (WH) features are those that
might be useful for future increments of the software, but that are not
currently scheduled to be taken forward (Brennan
2009). They may be similar to CH features, but that are
identified as outwith the scope of the development process due to
- Figure 2 provides a
basic example of a Scrum planning board incorporating the MoSCoW
method, with some sample features from our project. It was implemented
physically with a whiteboard and sticky notes.
Figure 2. A sample Scrum planning board with MoSCoW prioritised work items. Intuitively, one would assume that all must have items are implemented first; however, due to the iterative nature of agile, and ever changing product features, not all must have features or work items are identified early, and can often be added later in the development process with different priorities.
Overview of the Sprints
- Prior to our first Sprint, we performed a scoping study,
which involved a review of the literature relating to established
behaviour models from psychology, and also an extensive search through
the data literature to find data that could be used to parameterise the
first iteration of our model. This scoping study and the construction
of our domain model took place over the first 3 months of the project,
we report on the development Sprints for the remaining 9 months of the
project for which we produced a working model after each iteration.
- We chose to implement an attitude-behaviour model that
takes inspiration from the Theory of Planned Behaviour (TPB) (Azjen 1991). TPB presumes that
behaviour is determined by an intention to perform that behaviour, in
our case engaging in a drinking occasion or drinking to intoxication.
Intention itself is determined by three core components: attitudes
towards that behaviour based on the individual's (positive or negative)
evaluations of the likely outcomes of the behaviour, subjective
norms relating to how an individual's peer group (parents,
partners, friends) feels about the individual performing the behaviour,
and lastly perceived behavioural control which
relates to the perceptions the individual has regarding their ability
to perform the behaviour, as well as constraints relating to performing
that behaviour, such as income (which impacts on affordability of
- In creating any computational model, parameterisation from
data sources is vital from both a practical and a validation
perspective. In the context of our research, parameterisation takes the
form of using individual-level respondent data to operationalise a
model of TPB. Upon analysis of the available resources in the UK Data
Archive, it became evident that we could not ideally parameterise the
TPB. The only population-wide source of alcohol related attitudinal
variables is the Offending Crime and Justice Survey 2003 (Home Office 2008). The decade
between 2000 and 2010 has seen several interesting population-level
phenomena emerge (e.g. a rise and subsequent fall in consumption for
the general population) and so this period of time was deemed a useful
window over which to simulate.
- Analysis of the OCJS data set revealed variables detailing
the self-reported frequency of drinking (fdrink) and frequency of
drinking to intoxication (fdrunk) status. Intoxication is an important
area of study as it is associated with a variety of acute alcohol
related harms including: road traffic accidents (Ridolfo & Stevenson 2001),
both unintentional (English et al.
1995) and intentional (English
et al. 1995; Single et
al. 1996) injuries,
and also absenteeism from work (Roche
et al. 2008). However fdrunk was deemed of less priority in
the Sprint as historical fdrunk data for validation purposes is not
- For the platform implementation of our model, subsequent to
an initial scoping study and domain analysis, we opted to code the
model without reliance on existing agent-based modelling platforms or
libraries. Whilst the use of existing libraries might offer efficiency
benefits in terms of code reuse, we preferred the additional
flexibility offered by a fully bespoke solution and had the programming
skills in the team to make such an approach feasible.
- In all, we undertook six development sprints during the
research effort. The balance of time was spent writing manuscripts
(including for this Special Issue of the Journal of
Societies and Social Simulation), conference visits and
macro-level dynamic modelling for both the current project and an
additional collaborative project with research partners. We summarize
the six sprints below.
First iteration: an object-oriented agent-based microsimulation of drinking frequency
- The OCJS was chosen as a data set to operationalise TPB in
our model, with consumption represented in the dataset by an
individual's fdrink. The goal for this Sprint was to develop an
agent-based model recreating fdrink trends in England between 2003 and
Sprint planning and the Sprint
- In our project, each Sprint was time boxed with a duration
of one month. For this Sprint, agents within our ABM represent
individual respondents from the OCJS, and encode traditional
demographic variables (age, gender, education), as well as TPB-related
agent parameters: alcohol related attitudes (individual drinks to: feel
relaxed, forget problems, feel friendly/outgoing, get drunk); a proxy
for norms (count of the types of groups an individual drinks with); and
proxies for perceived control (specific social roles held by the
individual, the number of unique places the individual normally drinks
in, and income). We define social roles as parenthood (dependent child
in household), partnership (cohabitation) and paid labour (holding a
salaried income) – denoted PPP.
- During the Scrum Planning meeting, the team took advice
from a statistical expert (Abdallah) who identified that a cumulative
logit model (CLM) (Agresti 2013)
would be suitable for modelling changes in the categorical fdrink
variable. Based on this discussion, several features required to
implement a CLM were added to our Product and Sprint Backlogs.
- To introduce dynamics to the CLM, input parameters must
vary. Aging is an obvious change that would occur during the
simulation; however, aging could only go so far in explaining the
dynamics of agent drinking states. We therefore anticipated the need
for alternative sources of dynamics, such as changes in: education,
income, and social role statuses. These variables can be measured
empirically, though changes in them are driven by wider social
phenomena, therefore predicting variable trajectories is not a trivial
problem. As a result, the stakeholders (Alan, Daniel, Paul and Robin)
agreed on features designed to incorporate exogenous inputs into our
model using empirically observed demographic and social role
trajectories. The trajectory feature was deemed a SH feature, as the
model could function without such exogenous inputs; it would just lack
face validity with regards to individual dynamics.
- Trajectories were acquired from a secondary data set, the British
Household Panel Survey (BHPS) (ISER
2010). The BHPS is a
longitudinal study with available data having been acquired between
1991 and 2009. These data provide trajectories for education (NFQ
levels) (Ofqual 2011),
income, as well as the three social roles. Upon initialisation, the
simulation matches agents (OCJS individuals) to trajectories extracted
from the BHPS based on an exact set of criteria (age-group, gender,
education, income and PPP status). Individual matches are then sampled
from at run-time and the trajectories extracted from those matches
provide dynamics for the underlying CLM.
- The CLM is fitted in R 3.0.2 and the intercepts and
coefficients are used as parameters for the agent-based simulation. The
ABM is implemented in Python v2.7.5. For the case study outlined in
this paper we utilise Bitbucket as the company's free pricing package
also offers a private repository option accessible to 5 individuals,
which was useful when our software was in the early stages of
development and not ready to be shared. All code related to this
project can be cloned from our Bitbucket repository (https://firstname.lastname@example.org/pyabm/pyabm.git).
- The first iteration was completed with no major problems;
however, the matching algorithm had a performance issue. The algorithm
was a crude implementation that, for every individual in the OCJS data
set, then searched every individual in the BHPS data set to find a
match, storing matches for each individual. Such an approach is
computationally expensive and involves N*M comparisons (length of BHPS
* length of OCJS), and led to a significant increase in simulation
initialisation time. As a result, we decided to add a CH feature to the
next Sprint, which was to tune the matching algorithm to improve
Second iteration: parameter estimation of the TPB model
- A hypothesis we were interested in exploring was whether
there existed multiple models that could explain historical drinking,
but which could render very different future trajectories of
consumption – based on ideas in Byrne (1998).
To this end, we conceived
a second sprint to seek out such models using computational
- The goal was to develop an evolutionary optimizer,
incorporating niching (Goldberg
& Richardson 1987), capable of identifying
parameterisations of the CLM that describe observed fdrink dynamics
Sprint planning and the Sprint
- The research team – specifically Robin – has expertize in
evolutionary optimization and has an existing toolbox developed for the
Matlab environment. Given the time constraints of the sprint, we
decided to re-use functionality from the toolbox. Since the ABM was
written in python, an early task in this Sprint for Daniel was to
develop a Matlab wrapper for the ABM. In parallel, Robin developed the
Matlab scripts and, with the wrapper in place, performed the analysis.
- The optimizer was able to find a family of models that
could recreate historical drinking trends. The one-step-ahead
predictions of each model were combined to provide an ensemble forecast
for fdrink in 2010. The method and results have been published in
Purshouse et al. (2014).
Third iteration: modelling drinking to intoxication
- Having established a baseline model that was capable of
simulating patterns in fdrink over time, we were now interested in
introducing additional fdrunk functionality, and in improving the
performance of the matching algorithm.
- The Sprint Goal was to introduce fdrunk functionality into
the existing model, and implement various code optimisations to improve
the performance of the simulation.
Sprint planning and the Sprint
- The matching algorithm code optimisation was upgraded from
a CH to a MH feature, as the performance degradation of the original
algorithm increased the time required to test and run the model. A
caching approach to the matching criteria was used to reduce the
complexity of the algorithm.
- Implementing the fdrunk functionality involved fitting a
CLM to predict probabilities of fdrunk states conditional on fdrink.
Erroneous data was removed, for example, respondents who report an
fdrink of once a month yet an fdrunk of most
days. Additional intercept and coefficient parameters were
added to the simulation, along with implementations of functions to
calculate fdrunk probabilities and to sample from them.
- During the previous sprint, the team undertook a planned
research visit to the Centre for Addiction and Mental Health (CAMH),
Toronto, to engage alcohol epidemiological experts in the project. A
new MH requirement emerged from the visit: ensuring that the model –
amongst its drinking patterns outputs – included a measure of alcohol
exposure (in grams of ethanol per day). Such a measure would
enable the model to be linked to existing epidemiological models that
forecast volumes of alcohol-related harm for policymakers. Whilst this
had been a CH requirement originally, the team at CAMH felt that the
new modelling was sufficiently advanced and useful for work on the
epidemiological interface to be accelerated. By raising the requirement
to MH, the project would be well-positioned for future use as a tool
for policy appraisal relating to reducing alcohol-related harms.
Fourth iteration: introducing measures of consumption
- We had initially considered incorporating a consumption
metric into our model during the original scoping study; however, we
could not find a data set containing both the alcohol related
attitudinal data and consumption data. As a result we focussed mainly
on patterns of drinking and using the OCJS as our primary data set.
After feedback from CAMH we decided to revisit the issue of
- The fourth iteration of the model aimed to incorporate an
alcohol exposure measure into the ABM. Average consumption data is
present in a variety of studies, for example the Health
Survey for England (HSE) (NatCen
and UCL 2011) and the General Lifestyle Survey (GLF),
though not present in our primary data set, the OCJS. A third data set
was therefore required to allow us to impute plausible levels of
exposure for individuals. We decided to change our primary data set to
the GLF for two reasons. Firstly, the survey sample size is greater
than the OCJS after ensuring data completeness, giving a more
representative population sample. Secondly, the GLF contains mean
weekly alcohol units as a consumption metric, allowing us to estimate a
model that we could use to predict year-on-year consumption.
Sprint planning and the Sprint
- Drawing on further statistical expertise (Mark), we
constructed a Box-Cox regression (BCR) model that describes exposure
for GLF individuals based on their age, age group, gender, education,
income, parenthood, partnership, paid labour, fdrink, and various
interaction terms between these variables as covariates. The BCR model
was fitted in R 3.0.2. Change in exposure is then driven by changes in
demographics and the predicted year-on-year drinking frequency. As a SH
feature in the Sprint Backlog, we aimed to implement the BCR model in
python so that consumption levels were calculated within the ABM rather
than performed afterwards.
- With the GLF now acting as the primary data set,
TPB-related agent parameters were imputed from the OCJS using a similar
matching approach to that previously described. Life-course
trajectories were imputed from the BHPS as previously described.
- We previously described a requirement for our project that
we document the function of our alcohol model in order to ensure the
long-term viability of our software independent of developer. Whilst we
do not retrospectively create extensive software requirements
documents, we do utilise modelling tools to describe our simulation
software, specifically UML. The class diagram in Figure 3 depicts a simple relationship
between the two core system classes (excluding a utility class for data
input/output etc.). Figure 3
also contains two activity diagrams that concisely depict the sequence
of activities or events that drive the internal initialisation and
operation of both the simulation and its multiple agent objects.
- One of our intentions was to implement the BCR model in
python, though because of the in-Sprint improvements, including
stepwise model selection by AIC and correlated errors, and the
collaborative effort with Mark who is familiar with R and not python,
we decided to downgrade the priority of re-implementing BCR into the
python model to a CH feature.
Figure 3. Class and activity diagrams representing simulation and agent objects for model iteration three.
Fifth iteration: simulating attitude change
- We were unable to identify a UK data source which tracks
attitude variables related to alcohol consumption over time. This is
unfortunate since, for alcohol, attitudes have been shown to be
strongly correlated with behaviours in TPB studies (Cooke et al. 2014). We decided
to revisit the issue of attitude change and determine any alternative
means of modelling attitudes.
- The goal of this sprint was to adopt two methods for
dynamic individual-level attitude change. Firstly, the aim was to adopt
a scenario analysis approach using simple assumptions about attitude
responses of individuals either increasing, decreasing, or
varying over time. Secondly, using the
cross-sectional data from the OCJS in 2003, we constructed Markov
models to identify transition probabilities for attitude responses
conditional on respondent age.
Sprint planning and the Sprint
- The scenario analysis approach to attitude change was
conceptually the most straightforward of our two approaches to
implement, though is evidently not data-driven in any respect. The
motivation for exploring this method of attitude change was to
determine if such blanket assumptions regarding attitude change could
more accurately explain observed fdrink and consumption patterns over
the baseline, and to also provide a comparison against the second
approach using transition probabilities.
- The transition probabilities approach was a more complex
modelling endeavour, and involved careful consideration of transition
rules (structure of allowed transition matrices) for the four attitudes
included in our model, each with four point attitude responses ranging
from 0 (disagree strongly) to 3 (agree strongly). A parameter
estimation technique was employed, similar to the method described in
Purshouse et al. (2014),
to search over a range of transition probabilities that could explain
the OCJS 2003 observed attitude responses grouped by age.
- We were able to find transition probabilities to explain
the OCJS 2003 data, however, including those into our model was
insufficient for the model to accurately explain dynamics in fdrink and
consumption across the simulated period. The same was true for two of
the three scenarios in our scenario analysis. The model which included
decreasing attitude responses (increasingly negative responses
regarding alcohol for all attitude variables) proved the most able at
capturing trends in fdrink, particularly the decrease in most
days drinking seen over the past decade (the heaviest
drinking category). The cross-sectional attitude model is the more
data-driven of the approaches, but the failure of such a model to
replicate observed data reveals one of two things; that either the
cross-sectional data is not representative of attitude change, or that
the model is inherently missing certain factors to allow it to more
accurately predict history.
Sixth iteration: accounting for stochastic variance
- Previous research during our second Sprint had demonstrated
a use for evolutionary parameter estimation for finding different
candidate operationalisations of our microsimulation model that help to
explain observed historical drinking frequency data, however, our
findings did not account for stochastic uncertainty in the underlying
microsimulation, and also did not evaluate consumption. Aleatory
analysis (Alden et al. 2012)
later determined a minimum of 250 replicate microsimulation runs in
order to mitigate the effects of stochastic uncertainty in the
microsimulation, and after performing 250 simulation runs we can
generate a stable median prediction of frequency of drinking.
Therefore, for a given set of initial parameters, our consumption
estimator needed to be able to process data from 250 simulation runs to
find the most representative model outputs.
- This Sprint aimed to update our consumption estimator to
process the additional volume of simulation data produced since our
aleatory analysis stipulates a minimum of 250 simulation runs for any
experiment. Both the ABM and the estimator in essence form a hybrid
model which must be capable of being invoked using our evolutionary
Figure 4. Parameter estimation and evaluation of alcohol consumption distributions using a hybrid model approach.
Sprint planning and the Sprint
- This Sprint, focused on the hybrid model component of our
proposed workflow, was split into three key phases. The first body of
features were related to the creation and execution of compute cluster
scripts to allow multiple agent-based microsimulation runs to be run in
parallel using grid computing facilities. The second phase was to
modify our consumption estimator script to evaluate all of the
resulting agent output, compute consumption distributions for each
replicate run, and output the most representative consumption
distribution. The third phase is to integrate the hybrid model with and
updated version of the evolutionary optimizer created during our second
Sprint review and proposed sixth iteration
- Whilst the Sprint was completed, a review of the fifth
model iteration has highlighted a technical challenge that requires
addressing in a future Sprint. Our initial exploration into the use of
parameter estimation to determine candidate model parameters that best
explain historical trends in fdrink was successful (Purshouse et al. 2014), and
we had discussions around building upon that approach to explain
historical trends in exposure in addition to fdrink. However, with the
added execution time for the model to account for stochastic
uncertainty, and the additional time required by our estimator (which
now includes stepwise model selection to determine the best model fit
for the data for each replicate simulation run), the execution time of
our model during parameter estimation will increase by several orders
of magnitude. The current optimization approach uses an evolutionary
algorithm that is not tailored for expensive cost function evaluations,
so a further sprint will be required to design an algorithm that can
cope with a more limited number of calls to the ABM (e.g. through use
of surrogate models). Figure 4
illustrates the proposed future workflow for our model, and will form
the basis for the next Sprint.
- Agent interactions will be a key focus for future sprints. The OCJS data set that is used to parameterise some components of our individual agents contains information relating to the preferred drinking locations of individuals, and with whom they chose to drink. Such information will prove essential to maintaining a data-driven approach to our modelling effort, whilst at the same time providing a route into exploring location and social connectedness, providing the capacity of the ABSS model to exhibit emergent behaviour. The object-oriented agent-based approach we have adopted means that such additional functionality can be implemented whilst maintaining our existing code base. The Simulation and Agent classes (Figure 3), containing all the relevant code required to parameterise the model, load and schedule agents, perform agent behaviour, and output simulation data, will be maintained. The additional spatio-temporal and interaction functionality will be implemented by introducing concepts such as an abstract representation of an environment, which might itself comprise of a number of locations that are attributable to agents; these model aspects will be conceptualised using UML.
- Our case study in using the Scrum process to develop an ABM
of the dynamics of alcohol drinking patterns has proved instrumental in
us maintaining an evidence-based approach to developing our model. The
compartmentalisation of work into Sprints has focussed on data
gathering and modelling of individual behaviour prior to any
consideration of more traditional benefits of an agent-based modelling
approach, such as modelling environment and interaction. Whilst our
ultimate goal is to make use of the benefits that ABM can bring, Scrum
has forced us to strictly adhere to the scope of our initial
requirements for our model: we have developed microsimulations that
validate against historical dynamics for frequency of drinking. Further
research efforts will aim to extend this approach to social interaction
and possibly co-evolution – thus completing an adaptive generative
account (Hedström 2005; Room 2011) that is grounded in
the evidence base. The latter is crucial for acceptance of new methods
in the alcohol policy research field.
Evaluation of Scrum
- Scientific, empirical evaluation of the effectiveness and
utility of agile methods for software development is rare (Lindvall et al. 2002), and
where studies do exist they are limited to industry or near-industry
sectors. Salo and Abrahamsson (2004)
reported one such empirical review with a case study of the XP agile
methodology; demonstrating how qualitative and quantitative data can be
generated at different phases of XP agile, assisting in the evaluation
of the process. Other attempts to compare agile methods quantitatively
are limited to on paper attributes of agile
processes. An example is the evaluation framework described by Calo et
al. (2010), which the
authors used to compare Scrum and XP agile based on the emphasis each
process places on attributes such as individuals, teams, the process,
tools, customer negotiation and documentation. According to their
framework, XP is a more favourable agile methodology than Scrum (Calo et al. 2010, Table 2.). The
issue is that no evaluation framework is considered the exemplar, and
there is no way of determining how applicable each agile process is
given a particular customer, their requirements, and set of
stakeholders. Whilst empirical analysis of agile is needed, the
collection of the required evaluation data (e.g. stakeholder diaries,
documented code defects, process review documents) is outside the scope
of the current project.
- Without a formal evaluation of the Scrum process compared
to alternative SE methods, we cannot say categorically that Scrum is
the most applicable option for ABSS. In our opinion, however, Scrum has
brought benefits that have outweighed its costs. For example, in a busy
academic environment occasions do arise when it is difficult to have an
impromptu gathering of our interdisciplinary project team in order to
resolve development problems. If stakeholders are not immediately
available to sign off on any general requirements or feature changes,
development can be delayed. In these instances, our developer had to
adjust his daily work schedule to implement lower priority features,
including those that might not have been scheduled for the current
Sprint cycle, or improving existing code through refactoring. In such
instances, Revision Control (RC) played an integral part in maintaining
distinctly separate versions of the code, ensuring that non-functional
changes (refactored code), and features implemented yet not
stakeholder-agreed, remained distinct from the main Working Software
Increment. At the next available stakeholder meeting, additions could
then be signed off retrospectively; and the code merged into the
Working Software Increment. Alternatively, should stakeholders wish not
to include the already implemented functionality, the work item may
remain in the Product Backlog ready for the relevant Sprint; with full
or partial code instantly accessible through the RC repository. Another
issue with Scrum is that it can be argued that there is a time overhead
associated with the process (Scrum Planning/Scrum Review meetings,
organising the planning board etc.); however, we have found that this
time is more than recouped through the organisational efficiencies of
using the process.
- Predominantly, agile methods such as Scrum are more suited
to small teams of developers, thus it may seem as if such methods are
impractical for a one-two person development team such as ours;
however, this is not the case as we argue below.
- The principles that Scrum advocates, for example,
prioritisation of tasks, visualising and managing progress, and
incremental development, are ultimately beneficial as much to
individual productivity as they are to the team's productivity. There
have been occasions when Robin has also engaged as a developer; for
example, in the second Sprint; however, this involved close
collaboration with Daniel, as the optimiser had to directly interface
with the ABM.
- Adhering to Scrum forces our team to communicate regularly,
and to facilitate this we have thrice weekly or more short (15 minute)
status meetings between our developer and one or more stakeholders
(predominantly Daniel and Robin), with more detailed fortnightly
meetings with the members of the wider project team (Abdallah, Alan,
Mark, and Paul). These meetings provide an opportunity for rapid
feedback relating to the items in our Product Backlog, and allow our
developer to carefully assess the priorities for current and upcoming
Sprints. Our developer also prioritises daily tasks each morning,
during a one-person Scrum. The one-person Scrum provides time to set
the days goals, regardless of how trivial, and helps maximise
- Based on our experience of using Scrum, we have a produced
a list of simple recommendations on how ABSS developers can
operationalise Scrum within their project (Table 1).
We believe that Scrum could be extended to the full scope of a research
project, including the planning and management of non-development
related academic activities, such as manuscript preparation.
Table 1: Practical recommendations for the adoption of Scrum in an academic research project Recommendations when operationalising Scrum for a research project 1 Assign Scrum roles to the project team and familiarise them with The Scrum Guide (Schwaber & Sutherland 2013) to ensure members are aware of their responsibilities. This may be achieved with a half-day workshop or a hands-on tutorial session. 2 Establish a regular meeting schedule between project stakeholders and the development team. The development team should also organise as close to daily meetings as is feasible – this frequency of meetings should be adhered to, even for what might appear to be relatively long research work packages (e.g. 12 months). 3 Have a Scrum Master who follows the process as closely as is possible for your circumstances, despite any short-term pressures that might arise during the project to relapse to an unstructured approach. 4 Establish a Revision Control workflow and set up a source code repository before any coding begins. 5 Prioritise work items and functionality according to a method such as MoSCoW. 6 Fiercely adhere to the scope of each Sprint. Functionality can be always implemented in later Sprints; iteration is the key.
- This paper has described the adoption of an agile approach
to a real-world academic research project: the development of an
agent-based microsimulation of the dynamics of alcohol consumption. The
motivation for agile was driven by several project constraints
including, amongst others, a small development team, data issues and a
strict 12 month project duration. Requiring a development methodology
that would be resistant to potential changes and disruptions in the
project, and with a view to creating extendible and reusable software,
agile methods were an appealing choice for our situation.
- Despite the wealth of available agile methods, none were an
ideal fit for our project team; however, we chose to take inspiration
from principles of Scrum. Scrum has much in common with change-driven
project life cycles in project management (PMI
2013) which setting software aside, provides methods to
promote stakeholder engagement and deliver projects on time given a set
of resources and constraints. Given that we anticipated changing
software requirements, the Scrum process allowed us to organise our
project, and alongside the MoSCoW method helped us prioritise work
items to ensure incremental development of an early working prototype,
adding features and fixing code bugs in a principled manner.
- Purists might argue that the methods adopted by our small
one-two person development team might fall under the umbrella of other
agile approaches such as personal kanban rather
than Scrum. Personal kanban is another agile process by which to both
visualise and prioritise a body of required work (Benson & Barry 2011)
although it does not directly include guidelines for stakeholder
involvement since it is a methodology for individuals. Whilst it is
true we are not adhering to the letter of Scrum regarding roles, which
is not possible given the size of our development team, the way we
structure our work cycle, prioritise software features, and communicate
as a team, is most definitely in line with the Scrum ethos. Ultimately,
a team or individual should take inspiration from whatever agile
methods suit them and the needs of the project, with a goal to
delivering high quality scientific software through iterative and
- Agile methods generally promote a lean approach to software
documentation; however, for our study we chose to use UML to describe
the function of our model. For our relatively straightforward
implementation the UML is more than sufficient to convey the inner
workings of the software to future developers. As model complexity
increases, this approach may need re-evaluating, as the time overhead
involved with updating software documentation to account for an
evolving project may not be feasible.
- In conclusion, we argue that more ABSS researchers should adopt SE methods in future, choosing whichever specific practices and variants they feel are of best use to them. More case studies in agile software development will provide the ABSS and wider academic software development fields with further qualitative and possibly quantitative evidence on the utility of those agile methods.
- This work was funded by the UK Economic and Social Research Council under grant number ES/K001760/1. We would also like to thank researchers from the Centre for Research in Social Simulation (CRESS) at the University of Surrey and Jürgen Rehm and his team at the Centre for Addiction and Mental Health (CAMH) at the University of Toronto for helpful discussions, as well as the JASSS reviewers for their useful comments. The BHPS, GLF, HSE, and OCJS are Crown Copyright. The original data creators, depositors, or copyright holders, the funders of the data collections (when different), and the UK Data Archive bear no responsibility for analyses or interpretation of the data described in this report.
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